Traditional machine learning:


Machine learning (ML) technology plays an important role in prediction. ML has been developed through multiple generations and has formed a rich model structure, such as:

1. Linear regression.

Logistic regression.

3. Decision tree.

4. Support vector machines.

5. Bayesian model.

6. Regularize the model.

7. Model Ensemble.

Neural networks.



Select a model structure



with
training
data
model



learn
xi
Algorithm will
Lose,
The most
optimal
model





Using low power/simple models is better than using high power/complex models for the following reasons:

  • It will take a long time to train high-power models until we have a lot of processing power.
  • Before we have a large number of data, training high power model can result in over – fitting problems (because of the high power model with abundant parameters and can adapt to a wide range of data form, so we could end up a suitable for the specific to the current training data, rather than promote enough to keep prediction of future data).












god
the
net
winding
The back of
To:












Reinforcement learning:









The depth of the study
xi
+
Reinforcement learning
= AI


DL provides a more powerful prediction model than the classical ML technique, and generally produces good prediction results. Compared with the classical optimization model, reinforcement learning provides a faster learning mechanism and is more adaptable to environmental changes.

This article was translated by @Aliyunqi Community Organization.

Original title ‘How-AI-pegases-From-ML’

By Ricky Ho

The tiger said eight things.

The article is a brief translation. For more details, please refer to the original article